Advanced Optimization Methods for The Design of Aluminium Based Battery Enclosures for Electric Vehicles

气候变化是21世纪最重要的话题之一。交通运输部门是二氧化碳的主要排放源，必须适应以减少其对环境的影响。作为这一领域的一部分，汽车行业正专注于电动汽车，作为其减少二氧化碳排放的战略之一。随着电动汽车市场的增长，制造商正在开发专门针对电动汽车的新汽车平台。其中一个挑战是通过减少车身重量来补偿电池的增加重量。这一点尤其重要，因为额外的电池质量会降低电动汽车的行驶里程。因此，轻量化工程是车辆设计的关键部分，包括电池外壳的设计。电池外壳的铝挤压型材由于其高能量吸收重量比，是承受碰撞载荷的首选结构之一。这种电池外壳的设计过程是使用先进的计算机辅助工程工具进行和加速的，减少了物理测试。然而，这个过程仍然严重依赖于设计师的专业知识。基于材料选择等更精细的设计细节，以优化解决方案为目标的先进计算机辅助工程工具可以加速进入市场。这使得设计人员能够有效地设计和优化电池外壳，同时考虑材料特性和各种组装方法，如焊接、粘合或机械紧固。因此，实现轻量化结构，节省材料和资源。为此目的，必须制定一种被采用的方法。目的和目标提出的研究旨在开发电动汽车电池外壳的优化设计过程。本研究是材料科学、工程科学、计算机科学等多学科的交叉研究。重点关注材料工程的关键方面，材料系统的冲击和冲击性能，以及优化设计以增强铝挤压型材的拓扑结构。在考虑材料特性和冲击特性（如应变率依赖性）的情况下，应用机器学习技术提高结构拓扑优化的速度。拟议的研究将依靠随机方法来表示材料系统在多个长度尺度上的机械性能。提出的目标有助于提高和加快电池外壳结构的设计过程。研究方法的新颖性基于物理的神经网络，能够捕捉问题的潜在机制，通常被认为比纯粹的数据驱动方法更先进。通过结合材料系统、速率相关材料行为和其他特性等特性，这些网络可以在考虑物理特性的同时显著加快拓扑优化过程。这可以更快地优化和设计受冲击载荷的结构。此外，通过进行可变性分析，我们可以测量多种结构特性的影响，包括制造工艺和速率相关的材料行为，对结构拓扑的影响。提出的方法旨在加快和改进电池外壳轮廓的设计，同时确保它们符合碰撞安全要求。本项目属于EPSRC工程设计研究领域。EPSRC案例涉及工业合作伙伴Constellium。

DescriptionClimate change is one of the most important topics in the 21st century. The transportation sector, a major emitter of CO2, must adapt to reduce its impact on the environment. As a part of this sector, the automotive industry is focusing on electric vehicles as one of its strategies to reduce CO2 emissions. As the electric vehicle market grows, manufacturers are developing new vehicle platforms specifically for electric vehicles.One of the challenges is compensating for the added weight of batteries by decreasing vehicle body weight. This is especially important because the additional battery mass reduces the range of an electric vehicle. Thus, lightweight engineering is a crucial part of the vehicle's design, including the battery enclosure's design. Aluminium extrusion profiles for battery enclosures are one of the preferred structures subjected to crash loads, due to their high energy absorption to weight ratio.The design process of such battery enclosures is conducted and expedited using advanced computer-aided engineering tools, cutting down physical tests. Yet, the process still heavily relies on the designer's expertise. Advanced computer-aided engineering tools targeting optimised solutions, based on finer design details like material choice, can accelerate market entry. This, enabling designers to effectively design and optimize battery enclosures, considering material properties and various assembly methods such as welding, bonding, or mechanical fastening. Therefore, enabling lightweight structures that save material and resources. For this purpose, an adopted methodology must be developed.Aims and objectivesThe proposed research aims to develop an optimised design process for electric vehicle battery enclosures. The research is interdisciplinary bridging several disciplines, namely materials science, engineering science, and computer science.Focusing on critical aspects of materials engineering, impact and shock performance of material systems, and optimal design to enhance the topology of aluminium extrusion profile.Applying machine learning techniques for speed improvements of the structure's topology optimisation under consideration of the material properties and impact specific characteristics such as strain rate dependency.The proposed investigation will rely upon stochastic approaches to represent the mechanical performance of materials systems at multiple length scales. The proposed objectives help to enhance and accelerate the design process of battery enclosure structures.Novelty of the research methodologyPhysics-informed neural networks, capable of capturing the underlying mechanics of the problem, are often considered more advanced than purely data-driven methods. By incorporating properties like the material system, rate-dependent material behaviour, and other properties, these networks can notably speed up the topology optimization process while considering the physics. This enables faster optimisation and design of structures subjected to impact loads.Additionally, by conducting a variability analysis, we can gauge the impact of multiple structural properties, including the manufacturing process and rate-dependent material behaviour, on the structure's topology.The proposed methodology aims to expedite and improve the design of battery enclosure profiles while ensuring they meet crash safety requirements.EPSRCThis project falls within the EPSRC engineering design research area.Involved partnerThe EPSRC iCase involves the industrial partner Constellium.